Multispectral Imaging System + RGB

Highly integrated imaging system

Imaging system has one 20MP RGB camera and four 5MP multispectral cameras (green, red, red edge, and near infrared), enabling applications such as high-precision aerial surveying, crop growth monitoring, and natural resource surveys.

5MP multispectral camera

• Near-infrared (NIR) 860 nm ± 26 nm
• Red edge (RE) 730 nm ± 16 nm
• Red (R) 650 nm ± 16 nm
• Green (G) 560 nm ± 16 nm

RGB Camera Features

• 4/3 CMOS - 20MP Image Sensor
• 1/2000s - Fastest mechanical shutter speed
• 0.7s - High speed burst when using RGB camera

Sunlight sensor

The built-in sunlight sensor captures solar radiation and records it in an image file, allowing light compensation of the image data during 2D reconstruction. This results in more accurate NDVI results as well as increased accuracy and consistency of the acquired data over time.

RTK module

Precise images that capture every pixel. Mavic 3M with RTK module for centimeter-level positioning. The flight controller, camera, and RTK module synchronize within microseconds to accurately capture the location of each camera’s imaging center. This allows Mavic 3M to perform high-precision aerial surveys without using ground control points.

Data Capture

More specific flight parameter information on the ODM Quality Report.

Ground Control

Ground Control Points (GCPs) are crucial for agriculture drone mapping, especially for research purposes. They ensure the precise georeferencing of aerial images, correcting any distortions and aligning the maps with real-world coordinates. By using GCPs, researchers can obtain reliable and actionable data, enabling them to make informed decisions and improve agricultural practices.

Sun Angle

Processing Steps in WebODM

In our small plot research, we use WebODM to process multispectral imagery captured by UAVs. This ensures we can accurately compare treatments and gather valuable insights into crop performance. Here are the key steps involved:

1. Image Calibration:
   • We start by calibrating the multispectral images to ensure accurate color representation and reflectance values.
   • Calibration panels captured during the UAV flight are used to adjust the raw images for consistent lighting conditions.
2. Ground Control Points (GCPs):
   • Ground control points are incorporated to improve the spatial accuracy of the processed images.
   • These GCPs are markers placed in known locations within the field, surveyed with precise GPS coordinates.
3. Orthomosaicking:
   • The calibrated images are stitched together to create a single, seamless high-resolution orthomosaic.
   • This involves aligning and blending overlapping images to produce a detailed map of the entire field, correcting for any distortions.
4. Digital Surface Model (DSM) Generation:
   • Along with the orthomosaic, we generate a digital surface model that represents the elevation of the field’s surface.
   • This helps in understanding the topography and detecting any variations in elevation that might affect crop growth.
5. Quality Assurance:
   • A thorough quality check is conducted to ensure the accuracy and reliability of the processed outputs.
   • This includes validating the orthomosaic, DSM, and vegetation indices against the ground truth data and making necessary adjustments.

Further information available on the ODM Quality Report.

Processing steps in SimpleSense

1. Vegetation Indice Maps:
   • Image bands are manipulated to exploit features for segmentation or determine effect of imposed treatments.
2. Plot Layer Creation:
   • Plots are built in a GIS and integrated with customers experimental design.
3. Statistical Analysis:
   • Zonal statistics for imposed treatments.
   • Analysis of Variance (ANOVA) to test if there are significant differences in NDVI values between different treatment groups.
   • Post-hoc Analysis with Tukey’s HSD if ANOVA indicates significant differences, we use Tukey’s HSD test to identify which specific groups differ from each other.
4. Visualizations & Findings:
   • Figures and tables that are ready to be integrated
5. Outlier Detection:
   • In-season directive to potential outlier plots.
6. Organized Deliverables:
   • Report
   • Spatial data layers
   • Tabular data layers
   • Figures and tables
   • Statistical outputs
   • Imagery (if desired)

NDVI

The Normalized Difference Vegetation Index (NDVI) is a measure of vegetation health and density. For the Mavic 3M, the specific wavelengths for the bands are:

• RED band: 650 nm

• NIR band: 860 nm

The NDVI formula with these specific wavelengths is:

\[ \text{NDVI} = \frac{(\text{NIR}_{860} - \text{RED}_{650})}{(\text{NIR}_{860} + \text{RED}_{650})} \]

Where:

• NIR\(_{860}\) represents the near-infrared light (860 nm) reflected by vegetation.

• RED\(_{650}\) represents the red light (650 nm) reflected by vegetation.

NDRE

The Normalized Difference Red Edge Index (NDRE) is a measure of vegetation health and density, similar to NDVI, but it uses the red-edge band instead of the red band. It is calculated using the formula:

\[ \text{NDRE} = \frac{(\text{NIR} - \text{RE})}{(\text{NIR} + \text{RE})} \]

Where:

• NIR represents the near-infrared light reflected by vegetation.

• RE represents the red-edge light reflected by vegetation.

VARI

The Visible Atmospherically Resistant Index (VARI) is a measure of vegetation health that is less sensitive to atmospheric effects. It is calculated using the formula:

\[ \text{VARI} = \frac{(\text{GREEN} - \text{RED})}{(\text{GREEN} + \text{RED} - \text{BLUE})} \]

Where:

• GREEN represents the green light reflected by vegetation.

• RED represents the red light reflected by vegetation.

• BLUE represents the blue light reflected by vegetation.

ANOVA

ANOVA helps to test if there are significant differences in the mean vegetative reflectance values between different treatment groups. The formula for ANOVA is:

\[ F = \frac{\text{Between-group variability (Mean Square Between)}}{\text{Within-group variability (Mean Square Within)}} \]

where:

\[ \text{Mean Square Between} = \frac{\text{Sum of Squares Between}}{\text{Degrees of Freedom Between}} \]

\[ \text{Mean Square Within} = \frac{\text{Sum of Squares Within}}{\text{Degrees of Freedom Within}} \]

Tukey’s HSD Test

If ANOVA indicates significant differences, we use Tukey’s HSD test to identify which specific groups differ. The formula for Tukey’s HSD is:

\[ \text{HSD} = q_{\alpha} \sqrt{\frac{\text{Mean Square Within}}{n}} \]

where \(q_{\alpha}\) is the studentized range statistic and \(n\) is the sample size of each group.

Interpretation

1. ANOVA Results: Examine the ANOVA results to determine if there are significant differences between treatment groups based on the F-statistic and p-value.

2. Tukey’s HSD Results: Use Tukey’s HSD test results to identify which specific treatment groups have significantly different NDVI values.

Inference across multiple NDVI measures